Multilevel Models

Plausible Values as Predictors

Although the mixPV function was introduced as a way to analyze large scale assessments using multiple plausible values (PV), the function only works if the plausible values are used as the outcome (i.e., it is the Y variable or on the left hand side [LHS] of the equation). However, there are times when the PV is the predictor of interest. This still has to be analyzed properly (i.e., just don’t average all the values).

Jun 5, 2025

Working with missing data in large-scale assessments (without plausible values)

This is the syntax for accounting for missing data/imputing data with large scale assessments (without plausible values). This is Appendix A and accompanies the article: Huang, F., & Keller, B. (2025). Working with missing data in large-scale assessments. Large-scale Assessments in Education. doi: 10.1186/s40536-025-00248-9

Apr 17, 2025

Working with missing data in large-scale assessments (with plausible values)

This is the syntax for accounting for missing data/imputing data with large scale assessments (with plausible values). This accompanies the article: Huang, F., & Keller, B. (2025). Working with missing data in large-scale assessments. Large-scale Assessments in Education. doi: 10.1186/s40536-025-00248-9

Apr 17, 2025

Working with missing data in large-scale assessments

The article is open access. Additional syntax can also be seen here. An updated, corrected version of the article can be accessed here.

Apr 16, 2025

Reassessing weights in large-scale assessments and multilevel models

Mar 28, 2025

Cluster-robust standard errors with three-level data

Feb 1, 2025

Using plausible values when fitting multilevel models with large-scale assessment data using R

Article is open access. The mixPV function can now be accessed by installing the MLMusingR package.

Mar 1, 2024

Using robust standard errors for the analysis of binary outcomes with a small number of clusters

CR2 plug in for SPSS can be downloaded from: https://github.com/flh3/CR2

Jan 1, 2023

Practical multilevel modeling using R

Check out the latest info on the book here sample chapters additional code and an online appendix errata Some reviews: A major strength of this book is its accessibility. Huang effortlessly bridges the divide between the sometimes-abstruse literature on advanced statistics and the needs of applied researchers who lack extensive quantitative training. The result is an approachable text that covers all the basics, but also does not shy away from important advanced topics such as diagnostics, detecting and handling heteroscedasticity, and missing data handling methods. This book would make not only a useful guide to the application of multilevel modeling, but could also serve as an excellent companion text for a course on multilevel modeling. - Kristopher J. Preacher, Vanderbilt University

Jan 1, 2023

Accounting for heteroskedasticity resulting from between-group differences in multilevel models

Robust standard errors for multilevel models.

Jan 1, 2023